def colormap(plot):
""" Loads and plots the data for a time averaged map
Parameters
----------
plot : dictionary
func : a method that will plot the data on a specified map
Returns
-------
string : name of the plot
"""
print 'plotting map of ' + plot['variable']
# load data from netcdf file
data, lon, lat, depth, units, _, weights = pl.dataload(plot['ifile'], plot['variable'],
plot['dates'], realm=plot['realm_cat'],
scale=plot['scale'], shift=plot['shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], datatype=plot['data_type'],
cdostring=plot['cdostring'],
gridweights = True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
if plot['data_type'] == 'trends':
data, units = _trend_units(data, units, plot)
if plot['units']:
units = plot['units']
# get data at correct depth
data = _depth_data(data, depth, plot)
if plot['sigma'] and plot['data_type'] == 'trends':
detrenddata, _, _, _, _, _, _ = pl.dataload(plot['ifile'], plot['variable'],
plot['dates'], realm=plot['realm_cat'],
scale=plot['scale'], shift=plot['shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], datatype='detrend')
detrenddata = _full_depth_data(detrenddata, depth, plot)
siggrid = trend_significance(detrenddata, plot['sigma'])
cvalues, _ = _trend_units(siggrid, units, plot)
else:
cvalues = None
dft.filltitle(plot)
anom = True if plot['divergent'] or plot['data_type'] == 'trends' else False
label = stats(plot, data, weights=weights, rmse=False)
_pcolor(data, plot, anom=anom)
# make plot
pr.worldmap(plot['plot_projection'], lon, lat, data, ax_args=plot['data1']['ax_args'], label=label,
pcolor_args=plot['data1']['pcolor_args'], cblabel=units, plot=plot, cvalues=cvalues,
**plot['plot_args'])
plot_name = plotname(plot)
savefigures(plot_name, **plot)
plot['units'] = units
return plot_name
def scatter(plot):
print 'plotting scatter map of ' + plot['variable'] + ' and ' + plot[
'extra_variables'][0]
# load data from netcdf file
data, lon, lat, depth, units, _, weights = pl.dataload(
plot['ifile'],
plot['variable'],
plot['dates'],
realm=plot['realm_cat'],
scale=plot['scale'],
shift=plot['shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
datatype=plot['data_type'],
cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
# get data at correct depth
data = _depth_data(data, depth, plot)
data2, _, _, _, units2, _, _ = pl.dataload(
plot['extra_ifiles'][plot['extra_variables'][0]],
plot['extra_variables'][0],
plot['dates'],
realm=plot['extra_realm_cats'][plot['extra_variables'][0]],
scale=plot['extra_scales'][0],
shift=plot['shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
datatype=plot['data_type'],
cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['plot_depth']])
if plot['data_type'] == 'trends':
data, units = _trend_units(data, units, plot)
data2, units2 = _trend_units(data2, units2, plot)
if plot['units']:
units = plot['units']
if not plot['data1']['title_flag']:
plot['data1']['ax_args'][
'title'] = plot['data_type'] + ' ' + plot['model_ID'] + ' ' + plot[
'dates']['start_date'] + ' - ' + plot['dates']['end_date']
if 'xlabel' not in plot['data1']['ax_args']:
plot['data1']['ax_args']['xlabel'] = plot['variable'] + ' ' + units
if 'ylabel' not in plot['data1']['ax_args']:
plot['data1']['ax_args'][
'ylabel'] = plot['extra_variables'][0] + ' ' + units2
# make plot
pr.scatter(data, data2, ax_args=plot['data1']['ax_args'], plot=plot)
plot_name = plotname(plot)
savefigures(plot_name, **plot)
plot['units'] = units
return plot_name
def taylor_load(plot, compfile, depth, i, color, refdata, weights):
data, _, _, _, _, _, _ = pl.dataload(
compfile,
plot['variable'],
plot['comp_dates'],
realm=plot['realm_cat'],
scale=plot['comp_scale'],
shift=plot['comp_shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
datatype=plot['data_type'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=depth)
corr, refstd, std = weighted_correlation(refdata, data, weights)
stats_dictionary(plot, compfile, depth, std, std / refstd, corr)
return {
'name': plot['comp_model'],
'corrcoef': corr,
'std': std / refstd,
'color': color,
'marker': i,
'zorder': 2
}
def scatter(plot):
print 'plotting scatter map of ' + plot['variable'] + ' and ' + plot['extra_variables'][0]
# load data from netcdf file
data, lon, lat, depth, units, _, weights = pl.dataload(plot['ifile'], plot['variable'],
plot['dates'], realm=plot['realm_cat'],
scale=plot['scale'], shift=plot['shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], datatype=plot['data_type'],
cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
# get data at correct depth
data = _depth_data(data, depth, plot)
data2, _, _, _, units2, _, _ = pl.dataload(plot['extra_ifiles'][plot['extra_variables'][0]],
plot['extra_variables'][0],
plot['dates'],
realm=plot['extra_realm_cats'][plot['extra_variables'][0]],
scale=plot['extra_scales'][0], shift=plot['shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], datatype=plot['data_type'],
cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['plot_depth']])
if plot['data_type'] == 'trends':
data, units = _trend_units(data, units, plot)
data2, units2 = _trend_units(data2, units2, plot)
if plot['units']:
units = plot['units']
if not plot['data1']['title_flag']:
plot['data1']['ax_args']['title'] = plot['data_type'] + ' ' + plot['model_ID'] + ' ' + plot['dates']['start_date'] + ' - ' + plot['dates']['end_date']
if 'xlabel' not in plot['data1']['ax_args']:
plot['data1']['ax_args']['xlabel'] = plot['variable'] + ' ' + units
if 'ylabel' not in plot['data1']['ax_args']:
plot['data1']['ax_args']['ylabel'] = plot['extra_variables'][0] + ' ' + units2
# make plot
pr.scatter(data, data2, ax_args=plot['data1']['ax_args'], plot=plot)
plot_name = plotname(plot)
savefigures(plot_name, **plot)
plot['units'] = units
return plot_name
def section(plot):
""" Loads and plots the data for a time average section map.
Parameters
----------
plot : dictionary
func : a method that will plot the data on a specified map
Returns
-------
string : name of the plot
"""
print 'plotting section of ' + plot['variable']
data, _, lat, depth, units, _, _ = pl.dataload(
plot['ifile'],
plot['variable'],
plot['dates'],
realm=plot['realm_cat'],
scale=plot['scale'],
shift=plot['shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
datatype=plot['data_type'],
section=True,
cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
if plot['data_type'] == 'trends':
data, units = _trend_units(data, units, plot)
if plot['units']:
units = plot['units']
dft.filltitle(plot)
anom = True if plot['divergent'] or plot['data_type'] == 'trends' else False
_pcolor(data, plot, anom=anom)
fig = plt.figure(figsize=(10, 3))
gs = gridspec.GridSpec(1, 1, width_ratios=[1, 1])
# plot the data
pr.section(lat,
depth,
data,
plot=plot,
ax=plt.subplot(gs[0, 0]),
ax_args=plot['data1']['ax_args'],
pcolor_args=plot['data1']['pcolor_args'],
cblabel=units)
plot_name = plotname(plot)
savefigures(plot_name, **plot)
plot['units'] = units
return plot_name
def histogram(plot):
values = {}
data, _, _, depth, units, _, _ = pl.dataload(plot['ifile'], plot['variable'],
plot['comp_dates'], realm=plot['realm_cat'],
scale=plot['comp_scale'], shift=plot['comp_shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], datatype=plot['data_type'],
yearmean=plot['yearmean'],
cdostring=plot['cdostring'], fieldmean=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
data = _1d_depth_data(data, depth, plot)
data, units = _trend_units(data, units, plot)
if plot['units']:
units = plot['units']
values = []
values.append({'name': plot['model_ID'],
'data': data,
'color': 'r'})
for o in plot['comp_obs']:
values.append({'name': o,
'data': _histogram_data(plot, plot['obs_file'][o]),
'color': 'b'})
for i in plot['comp_ids']:
values.append({'name': i,
'data': _histogram_data(plot, plot['id_file'][i]),
'color': 'y'})
for m in plot['comp_models']:
values.append({'name': m,
'data': _histogram_data(plot, plot['model_file'][m]),
'color': 'g'})
cmipdata = []
for f in plot['cmip5_files']:
try:
cmipdata.append(_histogram_data(plot, f))
except:
continue
dft.filltitle(plot)
plot['data1']['ax_args']['xlabel'] = 'Trends ' + plot['comp_dates']['start_date'][:4] + '-' + plot['comp_dates']['end_date'][:4] + ' (' + units + ')'
plot['data1']['ax_args']['ylabel'] = '# Realizations'
pr.histogram(cmipdata, values, ax_args=plot['data1']['ax_args'], plot=plot)
plot_name = plotname(plot)
savefigures(plot_name, **plot)
plot['units'] = units
return plot_name
def zonalmeandata(plot, compfile):
data, _, _, _, _, _, _ = pl.dataload(compfile, plot['variable'],
plot['comp_dates'], realm=plot['realm_cat'],
scale=plot['comp_scale'], shift=plot['comp_shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], datatype=plot['data_type'],
section=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['plot_depth']])
return data
def _histogram_data(plot, compfile):
data, _, _, _, _, _, _ = pl.dataload(compfile, plot['variable'],
plot['comp_dates'], realm=plot['realm_cat'],
scale=plot['comp_scale'], shift=plot['comp_shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], datatype=plot['data_type'],
yearmean=plot['yearmean'],
fieldmean=True, cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=plot['plot_depth'])
data, _ = _trend_units(data, '', plot)
return data
def _timeseries_data(plot, compfile):
data, _, _, _, _, time, _ = pl.dataload(compfile, plot['variable'],
plot['comp_dates'], realm=plot['realm_cat'],
scale=plot['comp_scale'], shift=plot['comp_shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], fieldmean=True,
cdostring=plot['cdostring'],
yearmean=plot['yearmean'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['plot_depth']])
return data, time
def zonalmeandata(plot, compfile):
data, _, _, _, _, _, _ = pl.dataload(
compfile,
plot['variable'],
plot['comp_dates'],
realm=plot['realm_cat'],
scale=plot['comp_scale'],
shift=plot['comp_shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
datatype=plot['data_type'],
section=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['plot_depth']])
return data
def taylor_load(plot, compfile, depth, i, color, refdata, weights):
data, _, _, _, _, _, _ = pl.dataload(compfile, plot['variable'],
plot['comp_dates'], realm=plot['realm_cat'],
scale=plot['comp_scale'], shift=plot['comp_shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], datatype=plot['data_type'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=depth)
corr, refstd, std = weighted_correlation(refdata, data, weights)
stats_dictionary(plot, compfile, depth, std, std / refstd, corr)
return {'name': plot['comp_model'],
'corrcoef': corr,
'std': std / refstd,
'color': color,
'marker': i,
'zorder': 2}
def section(plot):
""" Loads and plots the data for a time average section map.
Parameters
----------
plot : dictionary
func : a method that will plot the data on a specified map
Returns
-------
string : name of the plot
"""
print 'plotting section of ' + plot['variable']
data, _, lat, depth, units, _, _ = pl.dataload(plot['ifile'], plot['variable'],
plot['dates'], realm=plot['realm_cat'],
scale=plot['scale'], shift=plot['shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], datatype=plot['data_type'],
section=True, cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
if plot['data_type'] == 'trends':
data, units = _trend_units(data, units, plot)
if plot['units']:
units = plot['units']
dft.filltitle(plot)
anom = True if plot['divergent'] or plot['data_type'] == 'trends' else False
_pcolor(data, plot, anom=anom)
fig = plt.figure(figsize=(10,3))
gs = gridspec.GridSpec(1, 1, width_ratios=[1, 1])
# plot the data
pr.section(lat, depth, data, plot=plot, ax=plt.subplot(gs[0, 0]), ax_args=plot['data1']['ax_args'],
pcolor_args=plot['data1']['pcolor_args'], cblabel=units)
plot_name = plotname(plot)
savefigures(plot_name, **plot)
plot['units'] = units
return plot_name
def _timeseries_data(plot, compfile):
data, _, _, _, _, time, _ = pl.dataload(
compfile,
plot['variable'],
plot['comp_dates'],
realm=plot['realm_cat'],
scale=plot['comp_scale'],
shift=plot['comp_shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
fieldmean=True,
cdostring=plot['cdostring'],
yearmean=plot['yearmean'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['plot_depth']])
return data, time
def _histogram_data(plot, compfile):
data, _, _, _, _, _, _ = pl.dataload(
compfile,
plot['variable'],
plot['comp_dates'],
realm=plot['realm_cat'],
scale=plot['comp_scale'],
shift=plot['comp_shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
datatype=plot['data_type'],
yearmean=plot['yearmean'],
fieldmean=True,
cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=plot['plot_depth'])
data, _ = _trend_units(data, '', plot)
return data
def zonalmean(plot):
""" Loads and plots a time average of the zonal means
for each latitude. Loads and plots the data for comparison.
Parameters
----------
plot : dictionary
func : a method that will plot the data on a specified map
Returns
-------
string : name of the plot
"""
print 'plotting zonal mean of ' + plot['variable']
data, _, lat, depth, units, _, _ = pl.dataload(plot['ifile'], plot['variable'],
plot['dates'], realm=plot['realm_cat'],
scale=plot['scale'], shift=plot['shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], datatype=plot['data_type'],
section=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
if 'ylabel' not in plot['data1']['ax_args']:
if plot['units']:
plot['data1']['ax_args']['ylabel'] = plot['units']
else:
plot['data1']['ax_args']['ylabel'] = units
plot['units'] = units
# get data at the correct depth
plot['plot_depth'] = None
if data.ndim > 1:
plot['plot_depth'] = min(depth, key=lambda x: abs(x - plot['depth']))
try:
depth_ind = np.where(np.round(depth) == plot['plot_depth'])[0][0]
except:
print('Failed to extract depth ' + plot['plot_depth'] + ' for ' + plot['variable'])
depth_ind = 0
data = data[depth_ind, :]
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
dft.filltitle(plot)
# make plot
pr.zonalmean(lat, data, plot=plot, ax=ax, ax_args=plot['data1']['ax_args'], color='r', zorder=6)
handles = [mpatches.Patch(color='r', label=plot['model_ID'])]
# plot comparison data on the same axis
if plot['cmip5_file']:
plot['comp_model'] = 'cmip5'
data = zonalmeandata(plot, plot['cmip5_file'])
pr.zonalmean(lat, data, plot=plot, ax=ax, label=plot['comp_model'], color='k', zorder=4)
handles.append(mpatches.Patch(color='k', label='cmip5'))
for o in plot['comp_obs']:
plot['comp_model'] = o
data = zonalmeandata(plot, plot['obs_file'][o])
pr.zonalmean(lat, data, plot=plot, ax=ax, label=plot['comp_model'], color='b', zorder=5)
handles.append(mpatches.Patch(color='b', label=str(plot['comp_model'])))
for m in plot['comp_models']:
plot['comp_model'] = model
data = zonalmeandata(plot, plot['model_file'][m])
pr.zonalmean(lat, data, plot=plot, ax=ax, label=plot['comp_model'], color='g', zorder=2)
handles.append(mpatches.Patch(color='g', label=str(plot['comp_model'])))
for i in plot['comp_ids']:
plot['comp_model'] = i
data = zonalmeandata(plot, plot['id_file'][i])
pr.zonalmean(lat, data, plot=plot, ax=ax, label=plot['comp_model'], color='y', zorder=3)
handles.append(mpatches.Patch(color='y', label=str(plot['comp_model'])))
for f in plot['cmip5_files']:
try:
plot['comp_model'] = 'cmip'
data = zonalmeandata(plot, f)
pr.zonalmean(lat, data, plot=plot, ax=ax, color='0.75', zorder=1)
except:
continue
ax.legend(handles=handles, loc='center left', bbox_to_anchor=(1, 0.5))
plot_name = plotname(plot)
savefigures(plot_name, **plot)
return plot_name
def taylor(plot):
labelled_stats = []
unlabelled_stats = []
obs = plot['obs_file'].iterkeys().next()
plot['plot_depth'] = plot['depths'][0]
plot['stats'] = {}
for i, d in enumerate(plot['depths']):
refdata, _, _, depth, units, _, weights = pl.dataload(
plot['obs_file'][obs],
plot['variable'],
plot['comp_dates'],
realm=plot['realm_cat'],
scale=plot['comp_scale'],
shift=plot['comp_shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
datatype=plot['data_type'],
gridweights=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[d])
refstd = weighted_std(refdata, weights)
stats_dictionary(plot, obs, d, refstd, 1, 1)
data, _, _, _, _, _, _ = pl.dataload(
plot['ifile'],
plot['variable'],
plot['comp_dates'],
realm=plot['realm_cat'],
scale=plot['comp_scale'],
shift=plot['comp_shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
datatype=plot['data_type'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=depth)
corrcoef, _, std = weighted_correlation(refdata, data, weights)
labelled_stats.append({
'name': plot['model_ID'],
'corrcoef': corrcoef,
'std': std / refstd,
'color': 'red',
'marker': '$%d$' % (i + 1),
'zorder': 3
})
stats_dictionary(plot, plot['ifile'], d, std, std / refstd, corrcoef)
if plot['cmip5_file']:
plot['comp_model'] = 'cmip5'
labelled_stats.append(
taylor_load(plot, plot['cmip5_file'], depth, '$%d$' % (i + 1),
'k', refdata, weights))
for m in plot['comp_models']:
plot['comp_model'] = model
labelled_stats.append(
taylor_load(plot, plot['model_file'][m], depth,
'$%d$' % (i + 1), 'g', refdata, weights))
for c in plot['comp_ids']:
plot['comp_model'] = c
labelled_stats.append(
taylor_load(plot, plot['id_file'][c], depth, '$%d$' % (i + 1),
'y', refdata, weights))
for f in plot['cmip5_files']:
plot['comp_model'] = 'cmip'
try:
unlabelled_stats.append(
taylor_load(plot, f, depth, '$%d$' % (i + 1), '0.75',
refdata, weights))
except:
continue
depthlist = [
str(i + 1) + ': ' + str(d) for i, d in enumerate(plot['depths'])
]
label = ' '.join(depthlist)
if len(plot['depths']) <= 1:
for l in labelled_stats:
l['marker'] = '.'
for l in unlabelled_stats:
l['marker'] = '.'
label = None
dft.filltitle(plot)
pr.taylor_from_stats(labelled_stats,
unlabelled_stats,
obs_label=obs,
label=label,
ax_args=plot['data1']['ax_args'])
# plot['stats'] = {'obserations': {'standard deviation': float(refstd)}}
plot_name = plotname(plot)
plt.tight_layout()
savefigures(plot_name, **plot)
if not plot['units']:
plot['units'] = units
plot['comp_file'] = plot['obs_file']
return plot_name
def timeseries(plot):
print 'plotting timeseries comparison of ' + plot['variable']
data, _, _, depth, units, time, _ = pl.dataload(plot['ifile'], plot['variable'],
plot['dates'], realm=plot['realm_cat'],
scale=plot['scale'], shift=plot['shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], fieldmean=True,
yearmean=plot['yearmean'],
cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
plot['data1']['ax_args']['xlabel'] = 'Time'
if 'ylabel' not in plot['data1']['ax_args']:
if plot['units']:
plot['data1']['ax_args']['ylabel'] = plot['units']
else:
plot['data1']['ax_args']['ylabel'] = units
plot['units'] = units
plot['plot_depth'] = None
if data.ndim > 1:
plot['plot_depth'] = min(depth, key=lambda x: abs(x - plot['depth']))
try:
depth_ind = np.where(np.round(depth) == plot['plot_depth'])[0][0]
except:
print('Failed to extract depth ' + plot['plot_depth'] + ' for ' + plot['variable'])
depth_ind = 0
data = data[:, depth_ind]
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
dft.filltitle(plot)
# make plot
pr.timeseries(time, data, plot=plot, ax=ax, label=plot['model_ID'], ax_args=plot['data1']['ax_args'], color='r', zorder=6)
handles = [mpatches.Patch(color='r', label=plot['model_ID'])]
# plot comparison data on the same axis
if plot['cmip5_file']:
plot['comp_model'] = 'cmip5'
data, x = _timeseries_data(plot, plot['cmip5_file'])
pr.timeseries(x, data, plot=plot, ax=ax, label=plot['comp_model'], ax_args=plot['data1']['ax_args'], color='k', zorder=4)
handles.append(mpatches.Patch(color='k', label=str(plot['comp_model'])))
for o in plot['comp_obs']:
plot['comp_model'] = o
data, x = _timeseries_data(plot, plot['obs_file'][o])
pr.timeseries(x, data, plot=plot, ax=ax, label=plot['comp_model'], ax_args=plot['data1']['ax_args'], color='b', zorder=5)
handles.append(mpatches.Patch(color='b', label=str(plot['comp_model'])))
for model in plot['comp_models']:
plot['comp_model'] = model
data, x = _timeseries_data(plot, plot['model_file'][model])
pr.timeseries(x, data, plot=plot, ax=ax, label=plot['comp_model'], ax_args=plot['data1']['ax_args'], color='g', zorder=2)
handles.append(mpatches.Patch(color='g', label=str(plot['comp_model'])))
for i in plot['comp_ids']:
plot['comp_model'] = i
data, x = timeseriesdata(plot, plot['id_file'][i], depth)
pr.timeseries(x, data, plot=plot, ax=ax, label=plot['comp_model'], ax_args=plot['data1']['ax_args'], color='y', zorder=3)
handles.append(mpatches.Patch(color='y', label=str(plot['comp_model'])))
for f in plot['cmip5_files']:
try:
plot['comp_model'] = 'cmip'
data, x = _timeseries_data(plot, f)
pr.timeseries(x, data, plot=plot, ax=ax, label=None, ax_args=plot['data1']['ax_args'], color='0.75', zorder=1)
except:
continue
ax.legend(handles=handles, loc='center left', bbox_to_anchor=(1, 0.5))
ax.yaxis.set_major_formatter(ticker.ScalarFormatter(useOffset=False))
plot_name = plotname(plot)
savefigures(plot_name, **plot)
return plot_name
def multivariable_taylor(plot):
if 'depth' not in plot:
plot['depth'] = 0
labelled_stats = []
obs = plot['obs_file'].iterkeys().next()
plot['stats'] = {}
colors = plt.matplotlib.cm.jet(
np.linspace(0, 1,
len(plot['extra_variables']) + 1))
refdata, _, _, depth, units, _, weights = pl.dataload(
plot['obs_file'][obs],
plot['variable'],
plot['comp_dates'],
realm=plot['realm_cat'],
scale=plot['comp_scale'],
shift=plot['comp_shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
datatype=plot['data_type'],
gridweights=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['depth']])
refstd = weighted_std(refdata, weights)
data, _, _, _, _, _, _ = pl.dataload(
plot['ifile'],
plot['variable'],
plot['dates'],
realm=plot['realm_cat'],
scale=plot['scale'],
shift=plot['shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
datatype=plot['data_type'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['depth']])
corrcoef, _, std = weighted_correlation(refdata, data, weights)
labelled_stats.append({
'name': plot['variable'],
'corrcoef': corrcoef,
'std': std / refstd,
'color': colors[0],
'marker': '.',
'zorder': 3
})
stats_dictionary(plot, plot['ifile'], plot['depth'], std, std / refstd,
corrcoef)
for i, var in enumerate(plot['extra_variables']):
refdata, _, _, depth, units, _, weights = pl.dataload(
plot['extra_obs_files'][var],
var,
plot['comp_dates'],
realm=plot['extra_realm_cats'][var],
scale=plot['extra_comp_scales'][i],
shift=plot['extra_comp_shifts'][i],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
datatype=plot['data_type'],
gridweights=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['depth']])
refstd = weighted_std(refdata, weights)
data, _, _, _, _, _, _ = pl.dataload(
plot['extra_ifiles'][var],
var,
plot['dates'],
realm=plot['realm_cat'],
scale=plot['extra_scales'][i],
shift=plot['extra_shifts'][i],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
datatype=plot['data_type'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['depth']])
corrcoef, _, std = weighted_correlation(refdata, data, weights)
labelled_stats.append({
'name': var,
'corrcoef': corrcoef,
'std': std / refstd,
'color': colors[i + 1],
'marker': '.',
'zorder': 3
})
stats_dictionary(plot, plot['extra_ifiles'][var], plot['depth'], std,
std / refstd, corrcoef)
if not plot['data1']['title_flag']:
plot['data1']['ax_args'][
'title'] = plot['data_type'] + ' ' + plot['model_ID'] + ' ' + plot[
'dates']['start_date'] + ' - ' + plot['dates']['end_date']
pr.taylor_from_stats(labelled_stats, [],
obs_label='observations',
label=None,
ax_args=plot['data1']['ax_args'])
plot_name = plotname(plot)
plt.tight_layout()
savefigures(plot_name, **plot)
if not plot['units']:
plot['units'] = '--'
plot['comp_file'] = plot['obs_file']
return plot_name
def colormap(plot):
""" Loads and plots the data for a time averaged map
Parameters
----------
plot : dictionary
func : a method that will plot the data on a specified map
Returns
-------
string : name of the plot
"""
print 'plotting map of ' + plot['variable']
# load data from netcdf file
data, lon, lat, depth, units, _, weights = pl.dataload(
plot['ifile'],
plot['variable'],
plot['dates'],
realm=plot['realm_cat'],
scale=plot['scale'],
shift=plot['shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
months=plot['months'],
datatype=plot['data_type'],
cdostring=plot['cdostring'],
gridweights=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
if plot['data_type'] == 'trends':
data, units = _trend_units(data, units, plot)
if plot['units']:
units = plot['units']
# get data at correct depth
data = _depth_data(data, depth, plot)
if plot['sigma'] and plot['data_type'] == 'trends':
detrenddata, _, _, _, _, _, _ = pl.dataload(
plot['ifile'],
plot['variable'],
plot['dates'],
realm=plot['realm_cat'],
scale=plot['scale'],
shift=plot['shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
months=plot['months'],
datatype='detrend')
detrenddata = _full_depth_data(detrenddata, depth, plot)
siggrid = trend_significance(detrenddata, plot['sigma'])
cvalues, _ = _trend_units(siggrid, units, plot)
else:
cvalues = None
dft.filltitle(plot)
anom = True if plot['divergent'] or plot['data_type'] == 'trends' else False
label = stats(plot, data, weights=weights, rmse=False)
_pcolor(data, plot, anom=anom)
# make plot
pr.worldmap(plot['plot_projection'],
lon,
lat,
data,
ax_args=plot['data1']['ax_args'],
label=label,
pcolor_args=plot['data1']['pcolor_args'],
cblabel=units,
cbbounds=plot['data1']['cbbounds'],
plot=plot,
cvalues=cvalues,
**plot['plot_args'])
plot_name = plotname(plot)
savefigures(plot_name, **plot)
plot['units'] = units
return plot_name
def histogram(plot):
values = {}
data, _, _, depth, units, _, _ = pl.dataload(
plot['ifile'],
plot['variable'],
plot['comp_dates'],
realm=plot['realm_cat'],
scale=plot['comp_scale'],
shift=plot['comp_shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
datatype=plot['data_type'],
yearmean=plot['yearmean'],
cdostring=plot['cdostring'],
fieldmean=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
data = _1d_depth_data(data, depth, plot)
data, units = _trend_units(data, units, plot)
if plot['units']:
units = plot['units']
values = []
values.append({'name': plot['model_ID'], 'data': data, 'color': 'r'})
for o in plot['comp_obs']:
values.append({
'name': o,
'data': _histogram_data(plot, plot['obs_file'][o]),
'color': 'b'
})
for i in plot['comp_ids']:
values.append({
'name': i,
'data': _histogram_data(plot, plot['id_file'][i]),
'color': 'y'
})
for m in plot['comp_models']:
values.append({
'name': m,
'data': _histogram_data(plot, plot['model_file'][m]),
'color': 'g'
})
cmipdata = []
for f in plot['cmip5_files']:
try:
cmipdata.append(_histogram_data(plot, f))
except:
continue
dft.filltitle(plot)
plot['data1']['ax_args']['xlabel'] = 'Trends ' + plot['comp_dates'][
'start_date'][:4] + '-' + plot['comp_dates'][
'end_date'][:4] + ' (' + units + ')'
plot['data1']['ax_args']['ylabel'] = '# Realizations'
pr.histogram(cmipdata, values, ax_args=plot['data1']['ax_args'], plot=plot)
plot_name = plotname(plot)
savefigures(plot_name, **plot)
plot['units'] = units
return plot_name
def section_comparison(plot):
""" Loads and plots the data for a time averaged section map.
Loads and plots the data for comparison and plots the
difference between the data and the comparison data.
Parameters
----------
plot : dictionary
func : a method that will plot the data on a specified map
Returns
-------
string : name of the plot
"""
print 'plotting section comparison of ' + plot['variable']
data2, _, _, depth, _, _, _ = pl.dataload(plot['comp_file'], plot['variable'],
plot['comp_dates'], realm=plot['realm_cat'],
scale=plot['comp_scale'], shift=plot['comp_shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], datatype=plot['data_type'],
section=True, cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
data, _, lat, depth, units, _, _ = pl.dataload(plot['ifile'], plot['variable'],
plot['dates'], realm=plot['realm_cat'],
scale=plot['scale'], shift=plot['shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], datatype=plot['data_type'],
section=True, cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=list(depth))
if plot['data_type'] == 'trends':
data, units = _trend_units(data, units, plot)
data2, _ = _trend_units(data2, units, plot)
if plot['units']:
units = plot['units']
compdata = data - data2
dft.filltitle(plot)
anom = True if plot['divergent'] or plot['data_type'] == 'trends' else False
_comp_pcolor(data, data2, plot, anom=anom)
if plot['alpha'] and plot['data_type'] == 'climatology':
fulldata, _, _, _, _, _, _ = pl.dataload(plot['ifile'], plot['variable'],
plot['dates'], realm=plot['realm_cat'],
scale=plot['scale'], shift=plot['shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], depthneeded=list(depth),
section=True)
fulldata2, _, _, _, _, _, _ = pl.dataload(plot['comp_file'], plot['variable'],
plot['comp_dates'], realm=plot['realm_cat'],
scale=plot['comp_scale'], shift=plot['comp_shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], depthneeded=list(depth),
section=True)
pvalues = ttest(fulldata, fulldata2)
else:
pvalues = None
# make plots of data, comparison data, data - comparison data
fig = plt.figure(figsize=(6, 8))
gs = gridspec.GridSpec(3, 2, width_ratios=[20, 1])
pr.section(lat, depth, data, plot=plot, ax=plt.subplot(gs[0, 0]), ax_args=plot['data1']['ax_args'],
pcolor_args=plot['data1']['pcolor_args'], cblabel=units, cbaxis=plt.subplot(gs[0, 1]))
pr.section(lat, depth, data2, plot=plot, ax=plt.subplot(gs[1, 0]), ax_args=plot['data2']['ax_args'],
pcolor_args=plot['data2']['pcolor_args'], cblabel=units, cbaxis=plt.subplot(gs[1, 1]))
pr.section(lat, depth, compdata, anom=True, rmse=True, pvalues=pvalues,
alpha=plot['alpha'], plot=plot, ax=plt.subplot(gs[2, 0]), ax_args=plot['comp']['ax_args'],
pcolor_args=plot['comp']['pcolor_args'], cblabel=units, cbaxis=plt.subplot(gs[2, 1]))
plt.tight_layout()
plot_name = plotname(plot)
savefigures(plot_name, **plot)
plot['units'] = units
return plot_name
def colormap_comparison(plot):
""" Loads and plots the data for a time averaged map.
Loads and plots the data for comparison and plots the
difference between the data and the comparison data.
Parameters
----------
plot : dictionary
func : a method that will plot the data on a specified map
Returns
-------
string : name of the plot
"""
print 'plotting comparison map of ' + plot['variable']
# load data from netcdf file
data, lon, lat, depth, units, _, weights = pl.dataload(plot['ifile'], plot['variable'],
plot['dates'], realm=plot['realm_cat'],
scale=plot['scale'], shift=plot['shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], datatype=plot['data_type'],
cdostring=plot['cdostring'], gridweights=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
data = _depth_data(data, depth, plot)
data2, _, _, _, _, _, _ = pl.dataload(plot['comp_file'], plot['variable'],
plot['comp_dates'], realm=plot['realm_cat'],
scale=plot['comp_scale'], shift=plot['comp_shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], datatype=plot['data_type'],
cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['plot_depth']])
if plot['data_type'] == 'trends':
data, units = _trend_units(data, units, plot)
data2, _ = _trend_units(data2, units, plot)
if plot['units']:
units = plot['units']
if plot['alpha'] and plot['data_type'] == 'climatology':
fulldata, _, _, _, _, _, _ = pl.dataload(plot['ifile'], plot['variable'],
plot['dates'], realm=plot['realm_cat'],
scale=plot['scale'], shift=plot['shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], depthneeded=[plot['plot_depth']])
fulldata2, _, _, _, _, _, _ = pl.dataload(plot['comp_file'], plot['variable'],
plot['comp_dates'], realm=plot['realm_cat'],
scale=plot['comp_scale'], shift=plot['comp_shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], depthneeded=[plot['plot_depth']])
pvalues = ttest(fulldata, fulldata2)
else:
pvalues = None
if plot['sigma'] and plot['data_type'] == 'trends':
detrenddata, _, _, _, _, _, _ = pl.dataload(plot['ifile'], plot['variable'],
plot['dates'], realm=plot['realm_cat'],
scale=plot['scale'], shift=plot['shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], datatype='detrend')
detrenddata = _full_depth_data(detrenddata, depth, plot)
siggrid = trend_significance(detrenddata, plot['sigma'])
cvalues, _ = _trend_units(siggrid, units, plot)
detrenddata, _, _, _, _, _, _ = pl.dataload(plot['comp_file'], plot['variable'],
plot['comp_dates'], realm=plot['realm_cat'],
scale=plot['comp_scale'], shift=plot['comp_shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], datatype='detrend')
detrenddata = _full_depth_data(detrenddata, depth, plot)
siggrid = trend_significance(detrenddata, plot['sigma'])
c2values, _ = _trend_units(siggrid, units, plot)
else:
cvalues = None
c2values = None
try:
compdata = data - data2
except:
data2 = data2.transpose()
compdata = data - data2
anom = True if plot['divergent'] or plot['data_type'] == 'trends' else False
_comp_pcolor(data, data2, plot, anom=anom)
label1 = stats(plot, data, weights=weights, rmse=False)
label2 = stats(plot, data2, weights=weights, rmse=False)
label3 = stats(plot, compdata, weights=weights, rmse=True)
dft.filltitle(plot)
fig, (axl, axm, axr) = plt.subplots(3, 1, figsize=(8, 8))
# make plots of data, comparison data, data - comparison data
pr.worldmap(plot['plot_projection'], lon, lat, data, plot=plot, ax=axl, ax_args=plot['data1']['ax_args'],
pcolor_args=plot['data1']['pcolor_args'], cblabel=units, cvalues=cvalues, label=label1,
**plot['plot_args'])
pr.worldmap(plot['plot_projection'], lon, lat, data2, plot=plot, ax=axm, ax_args=plot['data2']['ax_args'],
pcolor_args=plot['data2']['pcolor_args'], cblabel=units, cvalues=c2values, label=label2,
**plot['plot_args'])
pr.worldmap(plot['plot_projection'], lon, lat, compdata, pvalues=pvalues, alpha=plot['alpha'], anom=True,
rmse=True, plot=plot, ax=axr, ax_args=plot['comp']['ax_args'], label=label3,
pcolor_args=plot['comp']['pcolor_args'], cblabel=units, **plot['plot_args'])
plot_name = plotname(plot)
savefigures(plot_name, **plot)
plot['units'] = units
return plot_name
def section_comparison(plot):
""" Loads and plots the data for a time averaged section map.
Loads and plots the data for comparison and plots the
difference between the data and the comparison data.
Parameters
----------
plot : dictionary
func : a method that will plot the data on a specified map
Returns
-------
string : name of the plot
"""
print 'plotting section comparison of ' + plot['variable']
data2, _, _, depth, _, _, _ = pl.dataload(
plot['comp_file'],
plot['variable'],
plot['comp_dates'],
realm=plot['realm_cat'],
scale=plot['comp_scale'],
shift=plot['comp_shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
datatype=plot['data_type'],
section=True,
cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
data, _, lat, depth, units, _, _ = pl.dataload(
plot['ifile'],
plot['variable'],
plot['dates'],
realm=plot['realm_cat'],
scale=plot['scale'],
shift=plot['shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
datatype=plot['data_type'],
section=True,
cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=list(depth))
if plot['data_type'] == 'trends':
data, units = _trend_units(data, units, plot)
data2, _ = _trend_units(data2, units, plot)
if plot['units']:
units = plot['units']
compdata = data - data2
dft.filltitle(plot)
anom = True if plot['divergent'] or plot['data_type'] == 'trends' else False
_comp_pcolor(data, data2, compdata, plot, anom=anom)
if plot['alpha'] and plot['data_type'] == 'climatology':
fulldata, _, _, _, _, _, _ = pl.dataload(plot['ifile'],
plot['variable'],
plot['dates'],
realm=plot['realm_cat'],
scale=plot['scale'],
shift=plot['shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
depthneeded=list(depth),
section=True)
fulldata2, _, _, _, _, _, _ = pl.dataload(plot['comp_file'],
plot['variable'],
plot['comp_dates'],
realm=plot['realm_cat'],
scale=plot['comp_scale'],
shift=plot['comp_shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
depthneeded=list(depth),
section=True)
pvalues = ttest(fulldata, fulldata2)
else:
pvalues = None
# make plots of data, comparison data, data - comparison data
fig = plt.figure(figsize=(6, 8))
gs = gridspec.GridSpec(3, 2, width_ratios=[20, 1])
pr.section(lat,
depth,
data,
plot=plot,
ax=plt.subplot(gs[0, 0]),
ax_args=plot['data1']['ax_args'],
pcolor_args=plot['data1']['pcolor_args'],
cblabel=units,
cbaxis=plt.subplot(gs[0, 1]))
pr.section(lat,
depth,
data2,
plot=plot,
ax=plt.subplot(gs[1, 0]),
ax_args=plot['data2']['ax_args'],
pcolor_args=plot['data2']['pcolor_args'],
cblabel=units,
cbaxis=plt.subplot(gs[1, 1]))
pr.section(lat,
depth,
compdata,
anom=True,
rmse=True,
pvalues=pvalues,
alpha=plot['alpha'],
plot=plot,
ax=plt.subplot(gs[2, 0]),
ax_args=plot['comp']['ax_args'],
pcolor_args=plot['comp']['pcolor_args'],
cblabel=units,
cbaxis=plt.subplot(gs[2, 1]))
plt.tight_layout()
plot_name = plotname(plot)
savefigures(plot_name, **plot)
plot['units'] = units
return plot_name
def zonalmean(plot):
""" Loads and plots a time average of the zonal means
for each latitude. Loads and plots the data for comparison.
Parameters
----------
plot : dictionary
func : a method that will plot the data on a specified map
Returns
-------
string : name of the plot
"""
print 'plotting zonal mean of ' + plot['variable']
data, _, lat, depth, units, _, _ = pl.dataload(
plot['ifile'],
plot['variable'],
plot['dates'],
realm=plot['realm_cat'],
scale=plot['scale'],
shift=plot['shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
datatype=plot['data_type'],
section=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
if 'ylabel' not in plot['data1']['ax_args']:
if plot['units']:
plot['data1']['ax_args']['ylabel'] = plot['units']
else:
plot['data1']['ax_args']['ylabel'] = units
plot['units'] = units
# get data at the correct depth
plot['plot_depth'] = None
if data.ndim > 1:
plot['plot_depth'] = min(depth, key=lambda x: abs(x - plot['depth']))
try:
depth_ind = np.where(np.round(depth) == plot['plot_depth'])[0][0]
except:
print('Failed to extract depth ' + plot['plot_depth'] + ' for ' +
plot['variable'])
depth_ind = 0
data = data[depth_ind, :]
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
dft.filltitle(plot)
# make plot
pr.zonalmean(lat,
data,
plot=plot,
ax=ax,
ax_args=plot['data1']['ax_args'],
color='r',
zorder=6)
handles = [mpatches.Patch(color='r', label=plot['model_ID'])]
# plot comparison data on the same axis
if plot['cmip5_file']:
plot['comp_model'] = 'cmip5'
data = zonalmeandata(plot, plot['cmip5_file'])
pr.zonalmean(lat,
data,
plot=plot,
ax=ax,
label=plot['comp_model'],
color='k',
zorder=4)
handles.append(mpatches.Patch(color='k', label='cmip5'))
for o in plot['comp_obs']:
plot['comp_model'] = o
data = zonalmeandata(plot, plot['obs_file'][o])
pr.zonalmean(lat,
data,
plot=plot,
ax=ax,
label=plot['comp_model'],
color='b',
zorder=5)
handles.append(mpatches.Patch(color='b',
label=str(plot['comp_model'])))
for m in plot['comp_models']:
plot['comp_model'] = model
data = zonalmeandata(plot, plot['model_file'][m])
pr.zonalmean(lat,
data,
plot=plot,
ax=ax,
label=plot['comp_model'],
color='g',
zorder=2)
handles.append(mpatches.Patch(color='g',
label=str(plot['comp_model'])))
for i in plot['comp_ids']:
plot['comp_model'] = i
data = zonalmeandata(plot, plot['id_file'][i])
pr.zonalmean(lat,
data,
plot=plot,
ax=ax,
label=plot['comp_model'],
color='y',
zorder=3)
handles.append(mpatches.Patch(color='y',
label=str(plot['comp_model'])))
for f in plot['cmip5_files']:
try:
plot['comp_model'] = 'cmip'
data = zonalmeandata(plot, f)
pr.zonalmean(lat, data, plot=plot, ax=ax, color='0.75', zorder=1)
except:
continue
ax.legend(handles=handles, loc='center left', bbox_to_anchor=(1, 0.5))
plot_name = plotname(plot)
savefigures(plot_name, **plot)
return plot_name
def timeseries(plot):
print 'plotting timeseries comparison of ' + plot['variable']
data, _, _, depth, units, time, _ = pl.dataload(
plot['ifile'],
plot['variable'],
plot['dates'],
realm=plot['realm_cat'],
scale=plot['scale'],
shift=plot['shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
fieldmean=True,
yearmean=plot['yearmean'],
cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
plot['data1']['ax_args']['xlabel'] = 'Time'
if 'ylabel' not in plot['data1']['ax_args']:
if plot['units']:
plot['data1']['ax_args']['ylabel'] = plot['units']
else:
plot['data1']['ax_args']['ylabel'] = units
plot['units'] = units
plot['plot_depth'] = None
if data.ndim > 1:
plot['plot_depth'] = min(depth, key=lambda x: abs(x - plot['depth']))
try:
depth_ind = np.where(np.round(depth) == plot['plot_depth'])[0][0]
except:
print('Failed to extract depth ' + plot['plot_depth'] + ' for ' +
plot['variable'])
depth_ind = 0
data = data[:, depth_ind]
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
dft.filltitle(plot)
# make plot
pr.timeseries(time,
data,
plot=plot,
ax=ax,
label=plot['model_ID'],
ax_args=plot['data1']['ax_args'],
color='r',
zorder=6)
handles = [mpatches.Patch(color='r', label=plot['model_ID'])]
# plot comparison data on the same axis
if plot['cmip5_file']:
plot['comp_model'] = 'cmip5'
data, x = _timeseries_data(plot, plot['cmip5_file'])
pr.timeseries(x,
data,
plot=plot,
ax=ax,
label=plot['comp_model'],
ax_args=plot['data1']['ax_args'],
color='k',
zorder=4)
handles.append(mpatches.Patch(color='k',
label=str(plot['comp_model'])))
for o in plot['comp_obs']:
plot['comp_model'] = o
data, x = _timeseries_data(plot, plot['obs_file'][o])
pr.timeseries(x,
data,
plot=plot,
ax=ax,
label=plot['comp_model'],
ax_args=plot['data1']['ax_args'],
color='b',
zorder=5)
handles.append(mpatches.Patch(color='b',
label=str(plot['comp_model'])))
for model in plot['comp_models']:
plot['comp_model'] = model
data, x = _timeseries_data(plot, plot['model_file'][model])
pr.timeseries(x,
data,
plot=plot,
ax=ax,
label=plot['comp_model'],
ax_args=plot['data1']['ax_args'],
color='g',
zorder=2)
handles.append(mpatches.Patch(color='g',
label=str(plot['comp_model'])))
for i in plot['comp_ids']:
plot['comp_model'] = i
data, x = timeseriesdata(plot, plot['id_file'][i], depth)
pr.timeseries(x,
data,
plot=plot,
ax=ax,
label=plot['comp_model'],
ax_args=plot['data1']['ax_args'],
color='y',
zorder=3)
handles.append(mpatches.Patch(color='y',
label=str(plot['comp_model'])))
for f in plot['cmip5_files']:
try:
plot['comp_model'] = 'cmip'
data, x = _timeseries_data(plot, f)
pr.timeseries(x,
data,
plot=plot,
ax=ax,
label=None,
ax_args=plot['data1']['ax_args'],
color='0.75',
zorder=1)
except:
continue
ax.legend(handles=handles, loc='center left', bbox_to_anchor=(1, 0.5))
ax.yaxis.set_major_formatter(ticker.ScalarFormatter(useOffset=False))
plot_name = plotname(plot)
savefigures(plot_name, **plot)
return plot_name
def taylor(plot):
labelled_stats = []
unlabelled_stats = []
obs = plot['obs_file'].iterkeys().next()
plot['plot_depth'] = plot['depths'][0]
plot['stats'] = {}
for i, d in enumerate(plot['depths']):
refdata, _, _, depth, units, _, weights = pl.dataload(plot['obs_file'][obs], plot['variable'],
plot['comp_dates'], realm=plot['realm_cat'],
scale=plot['comp_scale'], shift=plot['comp_shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], datatype=plot['data_type'],
gridweights=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[d])
refstd = weighted_std(refdata, weights)
stats_dictionary(plot, obs, d, refstd, 1, 1)
data, _, _, _, _, _, _ = pl.dataload(plot['ifile'], plot['variable'],
plot['comp_dates'], realm=plot['realm_cat'],
scale=plot['comp_scale'], shift=plot['comp_shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], datatype=plot['data_type'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=depth)
corrcoef, _, std = weighted_correlation(refdata, data, weights)
labelled_stats.append({'name': plot['model_ID'],
'corrcoef': corrcoef,
'std': std / refstd,
'color': 'red',
'marker': '$%d$' % (i+1),
'zorder': 3})
stats_dictionary(plot, plot['ifile'], d, std, std / refstd, corrcoef)
if plot['cmip5_file']:
plot['comp_model'] = 'cmip5'
labelled_stats.append(taylor_load(plot, plot['cmip5_file'], depth, '$%d$' % (i+1), 'k', refdata, weights))
for m in plot['comp_models']:
plot['comp_model'] = model
labelled_stats.append(taylor_load(plot, plot['model_file'][m], depth, '$%d$' % (i+1), 'g', refdata, weights))
for c in plot['comp_ids']:
plot['comp_model'] = c
labelled_stats.append(taylor_load(plot, plot['id_file'][c], depth, '$%d$' % (i+1), 'y', refdata, weights))
for f in plot['cmip5_files']:
plot['comp_model'] = 'cmip'
try:
unlabelled_stats.append(taylor_load(plot, f, depth, '$%d$' % (i+1), '0.75', refdata, weights))
except:
continue
depthlist = [str(i + 1) + ': ' + str(d) for i, d in enumerate(plot['depths'])]
label = ' '.join(depthlist)
if len(plot['depths']) <= 1:
for l in labelled_stats:
l['marker'] = '.'
for l in unlabelled_stats:
l['marker'] = '.'
label = None
dft.filltitle(plot)
pr.taylor_from_stats(labelled_stats, unlabelled_stats, obs_label=obs,
label=label, ax_args=plot['data1']['ax_args'])
# plot['stats'] = {'obserations': {'standard deviation': float(refstd)}}
plot_name = plotname(plot)
plt.tight_layout()
savefigures(plot_name, **plot)
if not plot['units']:
plot['units'] = units
plot['comp_file'] = plot['obs_file']
return plot_name
def multivariable_taylor(plot):
if 'depth' not in plot:
plot['depth'] = 0
labelled_stats = []
obs = plot['obs_file'].iterkeys().next()
plot['stats'] = {}
colors = plt.matplotlib.cm.jet(np.linspace(0,1,len(plot['extra_variables']) + 1))
refdata, _, _, depth, units, _, weights = pl.dataload(plot['obs_file'][obs], plot['variable'],
plot['comp_dates'], realm=plot['realm_cat'],
scale=plot['comp_scale'], shift=plot['comp_shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], datatype=plot['data_type'],
gridweights=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['depth']])
refstd = weighted_std(refdata, weights)
data, _, _, _, _, _, _ = pl.dataload(plot['ifile'], plot['variable'],
plot['dates'], realm=plot['realm_cat'],
scale=plot['scale'], shift=plot['shift'],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], datatype=plot['data_type'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['depth']])
corrcoef, _, std = weighted_correlation(refdata, data, weights)
labelled_stats.append({'name': plot['variable'],
'corrcoef': corrcoef,
'std': std / refstd,
'color': colors[0],
'marker': '.',
'zorder': 3})
stats_dictionary(plot, plot['ifile'], plot['depth'], std, std / refstd, corrcoef)
for i, var in enumerate(plot['extra_variables']):
refdata, _, _, depth, units, _, weights = pl.dataload(plot['extra_obs_files'][var], var,
plot['comp_dates'], realm=plot['extra_realm_cats'][var],
scale=plot['extra_comp_scales'][i], shift=plot['extra_comp_shifts'][i],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'], datatype=plot['data_type'],
gridweights=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['depth']])
refstd = weighted_std(refdata, weights)
data, _, _, _, _, _, _ = pl.dataload(plot['extra_ifiles'][var], var,
plot['dates'], realm=plot['realm_cat'],
scale=plot['extra_scales'][i], shift=plot['extra_shifts'][i],
remapf=plot['remap'], remapgrid=plot['remap_grid'],
seasons=plot['seasons'], datatype=plot['data_type'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['depth']])
corrcoef, _, std = weighted_correlation(refdata, data, weights)
labelled_stats.append({'name': var,
'corrcoef': corrcoef,
'std': std / refstd,
'color': colors[i + 1],
'marker': '.',
'zorder': 3})
stats_dictionary(plot, plot['extra_ifiles'][var], plot['depth'], std, std / refstd, corrcoef)
if not plot['data1']['title_flag']:
plot['data1']['ax_args']['title'] = plot['data_type'] + ' ' + plot['model_ID'] + ' ' + plot['dates']['start_date'] + ' - ' + plot['dates']['end_date']
pr.taylor_from_stats(labelled_stats, [], obs_label='observations',
label=None, ax_args=plot['data1']['ax_args'])
plot_name = plotname(plot)
plt.tight_layout()
savefigures(plot_name, **plot)
if not plot['units']:
plot['units'] = '--'
plot['comp_file'] = plot['obs_file']
return plot_name
def colormap_comparison(plot):
""" Loads and plots the data for a time averaged map.
Loads and plots the data for comparison and plots the
difference between the data and the comparison data.
Parameters
----------
plot : dictionary
func : a method that will plot the data on a specified map
Returns
-------
string : name of the plot
"""
print 'plotting comparison map of ' + plot['variable']
# load data from netcdf file
data, lon, lat, depth, units, _, weights = pl.dataload(
plot['ifile'],
plot['variable'],
plot['dates'],
realm=plot['realm_cat'],
scale=plot['scale'],
shift=plot['shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
datatype=plot['data_type'],
cdostring=plot['cdostring'],
gridweights=True,
external_function=plot['external_function'],
external_function_args=plot['external_function_args'])
data = _depth_data(data, depth, plot)
data2, _, _, _, _, _, _ = pl.dataload(
plot['comp_file'],
plot['variable'],
plot['comp_dates'],
realm=plot['realm_cat'],
scale=plot['comp_scale'],
shift=plot['comp_shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
datatype=plot['data_type'],
cdostring=plot['cdostring'],
external_function=plot['external_function'],
external_function_args=plot['external_function_args'],
depthneeded=[plot['plot_depth']])
if plot['data_type'] == 'trends':
data, units = _trend_units(data, units, plot)
data2, _ = _trend_units(data2, units, plot)
if plot['units']:
units = plot['units']
if plot['alpha'] and plot['data_type'] == 'climatology':
fulldata, _, _, _, _, _, _ = pl.dataload(
plot['ifile'],
plot['variable'],
plot['dates'],
realm=plot['realm_cat'],
scale=plot['scale'],
shift=plot['shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
depthneeded=[plot['plot_depth']])
fulldata2, _, _, _, _, _, _ = pl.dataload(
plot['comp_file'],
plot['variable'],
plot['comp_dates'],
realm=plot['realm_cat'],
scale=plot['comp_scale'],
shift=plot['comp_shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
depthneeded=[plot['plot_depth']])
pvalues = ttest(fulldata, fulldata2)
else:
pvalues = None
if plot['sigma'] and plot['data_type'] == 'trends':
detrenddata, _, _, _, _, _, _ = pl.dataload(
plot['ifile'],
plot['variable'],
plot['dates'],
realm=plot['realm_cat'],
scale=plot['scale'],
shift=plot['shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['seasons'],
datatype='detrend')
detrenddata = _full_depth_data(detrenddata, depth, plot)
siggrid = trend_significance(detrenddata, plot['sigma'])
cvalues, _ = _trend_units(siggrid, units, plot)
detrenddata, _, _, _, _, _, _ = pl.dataload(
plot['comp_file'],
plot['variable'],
plot['comp_dates'],
realm=plot['realm_cat'],
scale=plot['comp_scale'],
shift=plot['comp_shift'],
remapf=plot['remap'],
remapgrid=plot['remap_grid'],
seasons=plot['comp_seasons'],
datatype='detrend')
detrenddata = _full_depth_data(detrenddata, depth, plot)
siggrid = trend_significance(detrenddata, plot['sigma'])
c2values, _ = _trend_units(siggrid, units, plot)
else:
cvalues = None
c2values = None
try:
compdata = data - data2
except:
data2 = data2.transpose()
compdata = data - data2
anom = True if plot['divergent'] or plot['data_type'] == 'trends' else False
_comp_pcolor(data, data2, compdata, plot, anom=anom)
label1 = stats(plot, data, weights=weights, rmse=False)
label2 = stats(plot, data2, weights=weights, rmse=False)
label3 = stats(plot, compdata, weights=weights, rmse=True)
dft.filltitle(plot)
fig, (axl, axm, axr) = plt.subplots(3, 1, figsize=(8, 8))
# make plots of data, comparison data, data - comparison data
pr.worldmap(plot['plot_projection'],
lon,
lat,
data,
plot=plot,
ax=axl,
ax_args=plot['data1']['ax_args'],
pcolor_args=plot['data1']['pcolor_args'],
cblabel=units,
cbbounds=plot['data1']['cbbounds'],
cvalues=cvalues,
label=label1,
**plot['plot_args'])
pr.worldmap(plot['plot_projection'],
lon,
lat,
data2,
plot=plot,
ax=axm,
ax_args=plot['data2']['ax_args'],
pcolor_args=plot['data2']['pcolor_args'],
cblabel=units,
cbbounds=plot['data2']['cbbounds'],
cvalues=c2values,
label=label2,
**plot['plot_args'])
pr.worldmap(plot['plot_projection'],
lon,
lat,
compdata,
pvalues=pvalues,
alpha=plot['alpha'],
anom=True,
rmse=True,
plot=plot,
ax=axr,
ax_args=plot['comp']['ax_args'],
label=label3,
pcolor_args=plot['comp']['pcolor_args'],
cblabel=units,
cbbounds=plot['comp']['cbbounds'],
**plot['plot_args'])
plot_name = plotname(plot)
savefigures(plot_name, **plot)
plot['units'] = units
return plot_name